Medications that Regulate Gastrointestinal Transit Influence Inpatient Blood Glucose.

Objective: A multitude of factors affect a hospitalized individual's blood glucose (BG), making BG difficult to predict and manage. Beyond medications well established to alter BG, such as beta-blockers, there are likely many medications with undiscovered effects on BG variability. Identification of these medications and the strength and timing of these relationships has potential to improve glycemic management and patient safety. Materials and Methods: EHR data from 103,871 inpatient encounters over 8 years within a large, urban health system was used to extract over 500 medications, laboratory measurements, and clinical predictors of BG. Feature selection was performed using an optimized Lasso model with repeated 5-fold cross-validation on the 80% training set, followed by a linear mixed regression model to evaluate statistical significance. Significant medication predictors were then evaluated for novelty against a comprehensive adverse drug event database. Results: We found 29 statistically significant features associated with BG; 24 were medications including 10 medications not previously documented to alter BG. The remaining five factors were Black/African American race, history of type 2 diabetes mellitus, prior BG (mean and last) and creatinine. Discussion: The unexpected medications, including several agents involved in gastrointestinal motility, found to affect BG were supported by available studies. This study may bring to light medications to use with caution in individuals with hyper- or hypoglycemia. Further investigation of these potential candidates is needed to enhance clinical utility of these findings. Conclusion: This study uniquely identifies medications involved in gastrointestinal transit to be predictors of BG that may not well established and recognized in clinical practice.

How Much Does Intravenous Fluid Cause Hematocrit to Drop? Improving Interpretation of Hematocrit Toward Better Post-Hemorrhage Care.

INTRODUCTION: Hemorrhage is assessed, at least in part, via hematocrit testing. To differentiate unexpected drops in hematocrit because of ongoing hemorrhage versus expected drops as a result of known hemorrhage and intravenous fluid administration, we model expected post-operative hematocrit values accounting for fluid balance and intraoperative estimated blood loss (EBL) among patients without substantial post-operative bleeding. MATERIALS AND METHODS: We reviewed patient-level data from the electronic health record of an academic medical center for all non-pregnant adults admitted for elective knee or hip arthroplasty from November 2013 to September 2022 who did not require blood products. We used linear regression to evaluate the association between post-operative hematocrit and predictor variables including pre-operative hematocrit, intraoperative net fluid intake, blood volume, time from surgery to lab testing, EBL, patient height, and patient weight. RESULTS: We included 6,648 cases. Mean (SD) estimated blood volume was 4,804 mL (1023), mean net fluid intake was 1,121 mL (792), and mean EBL was 144 mL (194). Each 100 mL of EBL and 1,000 mL net positive fluid intake was associated with a decrease of 0.52 units (95% CI, 0.51-0.53) and 2.4 units (2.2-2.7) in post-operative hematocrit. Pre-operative hematocrit was the strongest predictor of post-operative hematocrit. Each 1-unit increase in pre-operative hematocrit was associated with a 0.70-unit increase (95% CI, 0.67-0.73) in post-operative hematocrit. Our estimates were robust to sensitivity analyses, and all variables included in the model were statistically significant with P <.005. CONCLUSION: Patient-specific data, including fluid received since the time of initial hemorrhage, can aid in estimating expected post-hemorrhage hematocrit values, and thus in assessing for the ongoing hemorrhage.

Pharmacist, nurse, and physician perspectives on the implementation of the pharmacist discharge care (pharm-dc) intervention: A qualitative study.

BACKGROUND: The PHARMacist Discharge Care (PHARM-DC) intervention is a pharmacist-led Transitions of Care (TOC) program intended to reduce 30-day hospital readmissions and emergency department visits which has been implemented at two hospitals in the United States. The objectives of this study were to: 1) explore perspectives surrounding the PHARM-DC program from healthcare providers, leaders, and administrators at both institutions, and 2) identify factors which may contribute to intervention success and sustainability. METHODS: Focus groups and interviews were conducted with pharmacists, physicians, nurses, hospital leaders, and pharmacy administrators at two institutions in the Northeastern and Western United States. Interviews were audio recorded and transcribed, with transcriptions imported into NVivo for qualitative analysis. Thematic analysis was performed using an iterative process, with two study authors independently coding transcripts to identify themes. RESULTS: Overall, 37 individuals participated in ten focus groups and seven interviews. The themes identified included: 1) Organizational, Pharmacist, and Patient Factors Contributing to Transitions of Care, 2) Medication Challenges in Transitions of Care at Admission and Discharge, 3) Transitions of Care Communication and Discharge Follow-up, and 4) Opportunities for Improvement and Sustainability. The four themes were mapped to the constructs of the CFIR and RE-AIM frameworks. Some factors facilitating intervention success and sustainability were accurate medication histories collected on admission, addressing medication barriers before discharge, coordinating discharge using electronic health record discharge features, and having a structured process for intervention training and delivery. Barriers to intervention implementation and sustainability included gaps in communication with other care team members, and variable pharmacist skills for delivering the intervention. This study identified that using educational resources to standardize the TOC process addressed the issue of variations in pharmacists' skills for delivering TOC interventions. CONCLUSIONS: Nurses, physicians, pharmacists, pharmacist leaders, and hospital administrators were in agreement regarding the usefulness of the PHARM-DC intervention, while acknowledging challenges in its implementation and opportunities for improvement. Future research should focus on developing training materials to standardize and scale the intervention, eliminating barriers to medication access pre-discharge, coordinating discharge across care team members, and communicating medication changes to primary care providers post-discharge.

Surgical appropriateness nudges: Developing behavioral science nudges to integrate appropriateness criteria into the decision making of spine surgeons.

BACKGROUND: Substantial variation exists in surgeon decision making. In response, multiple specialty societies have established criteria for the appropriate use of spine surgery. Yet few strategies exist to facilitate routine use of appropriateness criteria by surgeons. Behavioral science nudges are increasingly used to enhance decision making by clinicians. We sought to design "surgical appropriateness nudges" to support routine use of appropriateness criteria for degenerative lumbar scoliosis and spondylolisthesis. METHODS: The work reflected Stage I of the NIH Stage Model for Behavioral Intervention Development and involved an iterative, multi-method approach, emphasizing qualitative methods. Study sites included two large referral centers for spine surgery. We recruited spine surgeons from both sites for two rounds of focus groups. To produce preliminary nudge prototypes, we examined sources of variation in surgeon decision making (Focus Group 1) and synthesized existing knowledge of appropriateness criteria, behavioral science nudge frameworks, electronic tools, and the surgical workflow. We refined nudge prototypes via feedback from content experts, site leaders, and spine surgeons (Focus Group 2). Concurrently, we collected data on surgical practices and outcomes at study sites. We pilot tested the refined nudge prototypes among spine surgeons, and surveyed them about nudge applicability, acceptability, and feasibility (scale 1-5, 5 = strongly agree). RESULTS: Fifteen surgeons participated in focus groups, giving substantive input and feedback on nudge design. Refined nudge prototypes included: individualized surgeon score cards (frameworks: descriptive social norms/peer comparison/feedback), online calculators embedded in the EHR (decision aid/mapping), a multispecialty case conference (injunctive norms/social influence), and a preoperative check (reminders/ salience of information/ accountable justification). Two nudges (score cards, preop checks) incorporated data on surgeon practices and outcomes. Six surgeons pilot tested the refined nudges, and five completed the survey (83%). The overall mean score was 4.0 (standard deviation [SD] 0.5), with scores of 3.9 (SD 0.5) for applicability, 4.1 (SD 0.5) for acceptability, and 4.0 (SD 0.5), for feasibility. Conferences had the highest scores 4.3 (SD 0.6) and calculators the lowest 3.9 (SD 0.4). CONCLUSIONS: Behavioral science nudges might be a promising strategy for facilitating incorporation of appropriateness criteria into the surgical workflow of spine surgeons. Future stages in intervention development will test whether these surgical appropriateness nudges can be implemented in practice and influence surgical decision making.

Cumulative Update of a Systematic Overview Evaluating Interventions Addressing Polypharmacy.

Importance: Polypharmacy is associated with mortality, falls, hospitalizations, and functional and cognitive decline. The study of polypharmacy-related interventions has increased substantially, prompting the need for an updated, more focused systematic overview. Objective: To systematically evaluate and summarize evidence across multiple systematic reviews (SRs) examining interventions addressing polypharmacy. Evidence Review: A search was conducted of MEDLINE, the Cochrane Database of Systematic Reviews, and the Database of Abstracts of Reviews of Effects for articles published from January 2017-October 2022, as well as those identified in a previous overview (January 2004-February 2017). Systematic reviews were included regardless of study design, setting, or outcome. The evidence was summarized by 4 categories: (1) medication-related process outcomes (eg, potentially inappropriate medication [PIM] and potential prescribing omission reductions), (2) clinical and functional outcomes, (3) health care use and economic outcomes, and (4) acceptability of the intervention. Findings: Fourteen SRs were identified (3 from the previous overview), 7 of which included meta-analyses, representing 179 unique published studies. Nine SRs examined medication-related process outcomes (low to very low evidence quality). Systematic reviews using pooled analyses found significant reductions in the number of PIMs, potential prescribing omissions, and total number of medications, and improvements in medication appropriateness. Twelve SRs examined clinical and functional outcomes (very low to moderate evidence quality). Five SRs examined mortality; all mortality meta-analyses were null, but studies with longer follow-up periods found greater reductions in mortality. Five SRs examined falls incidence; results were predominantly null save for a meta-analysis in which PIMs were discontinued. Of the 8 SRs examining quality of life, most (7) found predominantly null effects. Ten SRs examined hospitalizations and readmissions (very low to moderate evidence quality) and 4 examined emergency department visits (very low to low evidence quality). One SR found significant reductions in hospitalizations and readmissions among higher-intensity medication reviews with face-to-face patient components. Another meta-analysis found a null effect. Of the 7 SRs without meta-analyses for hospitalizations and readmissions, all had predominantly null results. Two of 4 SRs found reductions in emergency department visits. Two SRs examined acceptability (very low evidence quality), finding wide variation in the adoption of polypharmacy-related interventions. Conclusions and Relevance: This updated systematic overview noted little evidence of an association between polypharmacy-related interventions and reduced important clinical and health care use outcomes. More evidence is needed regarding which interventions are most useful and which populations would benefit most.

Structure and Funding of Clinical Informatics Fellowships: A National Survey of Program Directors.

BACKGROUND: In 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures. OBJECTIVES: The aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses. METHODS: We created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021. RESULTS: We invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities. CONCLUSION: CI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.

Comprehensive Pharmacist-led Transitions-of-care Medication Management around Hospital Discharge Adds Modest Cost Relative to Usual Care: Time-and-Motion Cost Analysis.

Optimal medication management is important during hospitalization and at discharge because post-discharge adverse drug events (ADEs) are common, often preventable, and contribute to patient harms, healthcare utilization, and costs. Conduct a cost analysis of a comprehensive pharmacist-led transitions-of-care medication management intervention for older adults during and after hospital discharge. Twelve intervention components addressed medication reconciliation, medication review, and medication adherence. Trained, experienced pharmacists delivered the intervention to older adults with chronic comorbidities at 2 large U.S. academic centers. To quantify and categorize time spent on the intervention, we conducted a time-and-motion analysis of study pharmacists over 36 sequential workdays (14 519 min) involving 117 patients. For 40 patients' hospitalizations, we observed all intervention activities. We used the median minutes spent and pharmacist wages nationally to calculate cost per hospitalization (2020 U.S. dollars) from the hospital perspective, relative to usual care. Pharmacists spent a median of 66.9 min per hospitalization (interquartile range 46.1-90.1), equating to $101 ($86 to $116 in sensitivity analyses). In unadjusted analyses, study site was associated with time spent (medians 111 and 51.8 min) while patient primary language, discharge disposition, number of outpatient medications, and patient age were not. In this cost analysis, comprehensive medication management around discharge cost about $101 per hospitalization, with variation across sites. This cost is at least an order of magnitude less than published costs associated with ADEs, hospital readmissions, or other interventions designed to reduce readmissions. Work is ongoing to assess the current intervention's effectiveness.

Microvascular Aging and Eicosanoids-Women's Evaluation of Systemic Aging Tenacity ("You are never too old to become younger!") Specialized Center of Research Excellence on Sex Differences Career Enhancement Core: Building the Future.

The objective of the National Institutes of Health Office of Research in Women's Health (NIH/ORWH) Specialized Center of Research and Career Enhancement (SCORE) program is to expedite the development and application of new knowledge that affect women, to learn more about the etiology of these diseases, and to foster improved approaches to treatment and/or prevention. Each SCORE has a Career Enhancement Core (CEC) that serves to meet the career enhancement needs of translational science in the study of sex differences. The Microvascular Aging and Eicosanoids-Women's Evaluation of Systemic aging Tenacity (MAE-WEST) ("You are never too old to become younger!") Specialized Center of Research Excellence (SCORE) on Sex Differences will study pro- and anti-inflammatory responses and small vessel aging traits. As part of our SCORE CEC, we have advanced several initiatives to embed consideration of sex as a biological variable (SABV) into the infrastructure of our two CEC institutions. Unlike other professions, ongoing physician education through continuing medical education (CME) activities is required and embedded in the practice of medicine. The MAE-WEST SCORE in collaboration with the CSMC Clinical Scholars Program, the Center for Research in Women's Health and Sex-differences and the CSMC CME Office requires SABV and as Diversity, Equity, and Inclusion components in all CSMC CME programs. Clinical practice is also increasingly guided by evidence-based guidelines, with Class I recommendations resulting from clinical trials rather than expert consensus. It is essential that women be included in clinical trials proportionate to the prevalence and burden of disease. The MAE-WEST SCORE has developed our own unique CEC for providing novel educational, networking, funding opportunities, and translation to practice support. The developed best practices have found novel ways to enhance studies of women's health and SABV. We welcome visitors on-site and virtual to share with the broader academic and practicing community.

Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research.

OBJECTIVE: We performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer's disease and related dementias (ADRD), to advance their use in research and clinical care. MATERIALS AND METHODS: Starting with a previous scoping review of EHR phenotypes, we performed a cumulative update (April 2020 through March 1, 2023) using Pubmed, PheKB, and expert review with exclusive focus on ADRD identification. We included algorithms using EHR data alone or in combination with non-EHR data and characterized whether they identified patients at high risk of or with a current diagnosis of ADRD. RESULTS: For our cumulative focused update, we reviewed 271 titles meeting our search criteria, 49 abstracts, and 26 full text papers. We identified 8 articles from the original systematic review, 8 from our new search, and 4 recommended by an expert. We identified 20 papers describing 19 unique EHR phenotypes for ADRD: 7 algorithms identifying patients with diagnosed dementia and 12 algorithms identifying patients at high risk of dementia that prioritize sensitivity over specificity. Reference standards range from only using other EHR data to in-person cognitive screening. CONCLUSION: A variety of EHR-based phenotypes are available for use in identifying populations with or at high-risk of developing ADRD. This review provides comparative detail to aid in choosing the best algorithm for research, clinical care, and population health projects based on the use case and available data. Future research may further improve the design and use of algorithms by considering EHR data provenance.

Effect of the population health inpatient Medicare Advantage pharmacist intervention on hospital readmissions: A quasi-experimental controlled study.

BACKGROUND: The population health inpatient Medicare Advantage pharmacist (PHIMAP) intervention is a pharmacist-led, transitions-of-care intervention that aims to reduce hospital readmissions among Medicare Advantage beneficiaries. PHIMAP includes inpatient pharmacist participation in interdisciplinary rounds, admission and discharge medication reconciliation, pharmacy staff delivery of discharge medications to the bedside, personalized discharge medication lists and counseling, and communication with outpatient pharmacists through an electronic health record. OBJECTIVE: To evaluate the effect of the PHIMAP intervention on unplanned 30-day same-hospital readmissions among Medicare Advantage patients. METHODS: Those included were patients admitted to a large urban academic medical center between May 2018 and March 2020 who had a Medicare Advantage plan and were aged at least 18 years. A 2-group, quasi-experimental design was utilized. Control patients received the usual care, which included a best possible medication history and a postdischarge phone call. A multivariable logistic regression model was estimated to predict unplanned 30-day same-hospital readmissions. This study was a Hypothesis Evaluating Treatment Effectiveness study. RESULTS: In total, 884 patients were included. The majority were White (59.0%), non-Hispanic (87.7%), English speaking (90.5%), and older adults (median age, 75 years; interquartile range, 70-83 years). We detected no statistically significant association between the PHIMAP intervention and unplanned 30-day same-hospital readmissions (odds ratio [OR] = 0.91, 95% CI = 0.56-1.52). After adjusting for patient demographics and clinical covariates, significant predictors of 30-day readmissions included the number of emergency department/inpatient visits within 180 days prior to index admission (OR = 1.40, 95% CI = 1.11-1.77); discharge to a post-acute care facility, such as an inpatient rehabilitation facility, long-term acute care facility, or skilled nursing facility (OR = 1.69, 95% CI = 1.06-2.66); hospital length of stay in days (OR = 1.04, 95% CI=1.01-1.07); and the Agency for Healthcare Research and Quality Elixhauser Comorbidity Index score (OR = 1.01, 95% CI = 1.01-1.02). CONCLUSIONS: Significant predictors of readmissions among Medicare Advantage beneficiaries were consistent with greater illness severity, including a recent history of prior hospital utilization, a discharge to post-acute care facility (vs home), a longer length of hospital stay, and a higher comorbidity burden. Although we detected no statistically significant association between PHIMAP and unplanned 30-day same-hospital readmissions, differences in study group assignment based on the day of hospital discharge (weekend vs weekday) was a noted limitation of this study. Future studies of inpatient pharmacist-led interventions should plan to minimize the risk of selection bias due to differences in the time of patient discharge. DISCLOSURES: This study was supported in part by the National Institute on Aging under award number R01AG058911 (to Pevnick) and the UCLA Clinical Translational Science Institute (UL1 TR001881). The sponsor had no role in the design and conduct of the study, nor the writing of this report.

Identifying barriers and facilitators to deprescribing benzodiazepines and sedative hypnotics in the hospital setting using the Theoretical Domains Framework and the Capability, Opportunity, Motivation and Behaviour (COM-B) Model: a qualitative study.

OBJECTIVES: Geriatric guidelines strongly recommend avoiding benzodiazepines and non-benzodiazepine sedative hypnotics in older adults. Hospitalisation may provide an important opportunity to begin the process of deprescribing these medications, particularly as new contraindications arise. We used implementation science models and qualitative interviews to describe barriers and facilitators to deprescribing benzodiazepines and non-benzodiazepine sedative hypnotics in the hospital and develop potential interventions to address identified barriers. DESIGN: We used two implementation science models, the Capability, Opportunity and Behaviour Model (COM-B) and the Theoretical Domains Framework, to code interviews with hospital staff, and an implementation process, the Behaviour Change Wheel (BCW), to codevelop potential interventions with stakeholders from each clinician group. SETTING: Interviews took place in a tertiary, 886-bed hospital located in Los Angeles, California. PARTICIPANTS: Interview participants included physicians, pharmacists, pharmacist technicians, and nurses. RESULTS: We interviewed 14 clinicians. We found barriers and facilitators across all COM-B model domains. Barriers included lack of knowledge about how to engage in complex conversations about deprescribing (capability), competing tasks in the inpatient setting (opportunity), high levels of resistance/anxiety among patients to deprescribe (motivation), concerns about lack of postdischarge follow-up (motivation). Facilitators included high levels of knowledge about the risks of these medications (capability), regular rounds and huddles to identify inappropriate medications (opportunity) and beliefs that patients may be more receptive to deprescribing if the medication is related to the reason for hospitalisation (motivation). Potential modes of delivery included a seminar aimed at addressing capability and motivation barriers in nurses, a pharmacist-led deprescribing initiative using risk stratification to identify and target patients at highest need for deprescribing, and the use of evidence-based deprescribing education materials provided to patients at discharge. CONCLUSIONS: While we identified numerous barriers and facilitators to initiating deprescribing conversations in the hospital, nurse- and pharmacist-led interventions may be an appropriate opportunity to initiate deprescribing.

Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper.

Background: The objective of this paper is to describe the creation, validation, and comparison of two risk prediction modeling approaches for community-dwelling older adults to identify individuals at highest risk for adverse drug event-related hospitalizations. One approach will use traditional statistical methods, the second will use a machine learning approach. Methods: We will construct medication, clinical, health care utilization, and other variables known to be associated with adverse drug event-related hospitalizations. To create the cohort, we will include older adults (≥ 65 years of age) empaneled to a primary care physician within the Cedars-Sinai Health System primary care clinics with polypharmacy (≥ 5 medications) or at least 1 medication commonly implicated in ADEs (certain oral hypoglycemics, anti-coagulants, anti-platelets, and insulins). We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. We will use a variety of metrics to compare model performance and to assess the risk of algorithmic bias. Discussion: In conclusion, we hope to develop a pragmatic model that can be implemented in the primary care setting to risk stratify older adults to further optimize medication management.

Implementation of an Electronic Health Records-Based Safe Contrast Limit for Preventing Contrast-Associated Acute Kidney Injury After Percutaneous Coronary Intervention.

BACKGROUND: Contrast-associated acute kidney injury (CA-AKI) after percutaneous coronary intervention is associated with increased mortality. We assessed the effectiveness of an electronic health records safe contrast limit tool in predicting CA-AKI risk and reducing contrast use and CA-AKI. METHODS: We created an alert displaying the safe contrast limit to cardiac catheterization laboratory staff prior to percutaneous coronary intervention. The alert used risk factors automatically extracted from the electronic health records. We included procedures from June 1, 2020 to October 1, 2021; the intervention went live February 10, 2021. Using difference-in-differences analysis, we evaluated changes in contrast volume and CA-AKI rates after contrast limit tool implementation compared to control hospitals. Cardiologists were surveyed prior to and 9 months after alert implementation on beliefs, practice patterns, and safe contrast estimates for example patients. RESULTS: At the one intervention site, there were 508 percutaneous coronary interventions before and 531 after tool deployment. At 15 control sites, there were 3550 and 3979 percutaneous coronary interventions, respectively. The contrast limit predicted CA-AKI with an accuracy of 64.1%, negative predictive value of 93.3%, and positive predictive value of 18.7%. After implementation, in high/modifiable risk patients (defined as having a calculated contrast limit <500ml) there was a small but significant -4.60 mL/month (95% CI, -8.24 to -1.00) change in average contrast use but no change in CA-AKI rates (odds ratio, 0.96 [95% CI, 0.84-1.10]). Low-risk patients had no change in contrast use (-0.50 mL/month [95% CI, -7.49 to 6.49]) or CA-AKI (odds ratio, 1.24 [95% CI, 0.79-1.93]). In assessing CA-AKI risk, clinicians heavily weighted age and diabetes but often did not consider anemia, cardiogenic shock, and heart failure. CONCLUSIONS: Clinicians often used a simplified assessment of CA-AKI risk that did not include important risk factors, leading to risk estimations inconsistent with established models. Despite clinician skepticism, an electronic health records-based contrast limit tool more accurately predicted CA-AKI risk and was associated with a small decrease in contrast use during percutaneous coronary intervention but no change in CA-AKI rates.

Digital Tools Designed to Obtain the History of Present Illness From Patients: Scoping Review.

BACKGROUND: Many medical conditions, perhaps 80% of them, can be diagnosed by taking a thorough history of present illness (HPI). However, in the clinical setting, situational factors such as interruptions and time pressure may cause interactions with patients to be brief and fragmented. One solution for improving clinicians' ability to collect a thorough HPI and maximize efficiency and quality of care could be to use a digital tool to obtain the HPI before face-to-face evaluation by a clinician. OBJECTIVE: Our objective was to identify and characterize digital tools that have been designed to obtain the HPI directly from patients or caregivers and present this information to clinicians before a face-to-face encounter. We also sought to describe outcomes reported in testing of these tools, especially those related to usability, efficiency, and quality of care. METHODS: We conducted a scoping review using predefined search terms in the following databases: MEDLINE, CINAHL, PsycINFO, Web of Science, Embase, IEEE Xplore Digital Library, ACM Digital Library, and ProQuest Dissertations & Theses Global. Two reviewers screened titles and abstracts for relevance, performed full-text reviews of articles meeting the inclusion criteria, and used a pile-sorting procedure to identify distinguishing characteristics of the tools. Information describing the tools was primarily obtained from identified peer-reviewed sources; in addition, supplementary information was obtained from tool websites and through direct communications with tool creators. RESULTS: We identified 18 tools meeting the inclusion criteria. Of these 18 tools, 14 (78%) used primarily closed-ended and multiple-choice questions, 1 (6%) used free-text input, and 3 (17%) used conversational (chatbot) style. More than half (10/18, 56%) of the tools were tailored to specific patient subpopulations; the remaining (8/18, 44%) tools did not specify a target subpopulation. Of the 18 tools, 7 (39%) included multilingual support, and 12 (67%) had the capability to transfer data directly into the electronic health record. Studies of the tools reported on various outcome measures related to usability, efficiency, and quality of care. CONCLUSIONS: The HPI tools we identified (N=18) varied greatly in their purpose and functionality. There was no consensus on how patient-generated information should be collected or presented to clinicians. Existing tools have undergone inconsistent levels of testing, with a wide variety of different outcome measures used in evaluation, including some related to usability, efficiency, and quality of care. There is substantial interest in using digital tools to obtain the HPI from patients, but the outcomes measured have been inconsistent. Future research should focus on whether using HPI tools can lead to improved patient experience and health outcomes, although surrogate end points could instead be used so long as patient safety is monitored.

Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record. STUDY DESIGN: A retrospective cohort study of deliveries at an academic, tertiary care hospital was conducted from 2013 to 2019 who had at least two cervical examinations. The population was divided into those delivered by physicians with nulliparous term singleton vertex (NTSV) cesarean delivery rates <23.9% (Partometer cohort) and the remainder (control cohort). The cesarean rate among this population of lower risk patients is a standard metric by which to compare provider rates; <23.9% was the Healthy People 2020 goal. A supervised automated ML approach was applied to generate a model for each population. The primary outcome was accuracy of the model developed on the Partometer cohort at 4 hours from admission to labor and delivery. Secondary outcomes included discrimination ability (receiver operating characteristics-area under the curve [ROC-AUC]), precision-recall AUC, and calibration of the Partometer. To assess generalizability, we compared the performance and clinical predictors identified by the Partometer to the control model. RESULTS: There were 37,932 deliveries during the study period; after exclusions, 9,385 deliveries were included in the Partometer cohort and 19,683 in the control cohort. Accuracy of predicting vaginal delivery at 4 hours was 87.1% for the Partometer (ROC-AUC: 0.82). Clinical predictors of greatest importance in the stacked Intrapartum Partometer Model included the Admission Model prediction and ongoing measures of dilatation and station which mirrored those found in the control population. CONCLUSION: Using automated ML and intrapartum factors improved the accuracy of prediction of probability of a vaginal delivery over both previously published models based on logistic regression. Harnessing real-time data and ML could represent the bridge to generating a truly prescriptive tool to augment clinical decision-making, predict labor outcomes, and reduce maternal and neonatal morbidity. KEY POINTS: · Our ML-based model yielded accurate predictions of mode of delivery early in labor.. · Predictors for models created on populations with high and low cesarean rates were the same.. · A ML-based model may provide meaningful guidance to clinicians managing labor..

A qualitative dual-site analysis of the pharmacist discharge care (PHARM-DC) intervention using the CFIR framework.

INTRODUCTION: Older adults face several challenges when transitioning from acute hospitals to community-based care. The PHARMacist Discharge Care (PHARM-DC) intervention is a pharmacist-led Transitions of Care (TOC) program intended to reduce 30-day hospital readmissions and emergency department visits at two large hospitals. This study used the Consolidated Framework for Implementation Research (CFIR) framework to evaluate pharmacist perceptions of the PHARM-DC intervention. METHODS: Intervention pharmacists and pharmacy administrators were purposively recruited by study team members located within each participating institution. Study team members located within each institution coordinated with two study authors unaffiliated with the institutions implementing the intervention to conduct interviews and focus groups remotely via telecommunication software. Interviews were recorded and transcribed, with transcriptions imported into NVivo for qualitative analysis. Qualitative analysis was performed using an iterative process to identify "a priori" constructs based on CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of the individuals involved, and the process of implementation) and to create overarching themes as identified during coding. RESULTS: In total, ten semi-structured interviews and one focus group were completed across both hospitals. At Site A, six interviews were conducted with intervention pharmacists and pharmacists in administrative roles. Also at Site A, one focus group comprised of five intervention pharmacists was conducted. At Site B, interviews were conducted with four intervention pharmacists and pharmacists in administrative roles. Three overarching themes were identified: PHARM-DC and Institutional Context, Importance of PHARM-DC Adaptability, and Recommendations for PHARM-DC Improvement and Sustainability. Increasing pharmacist support for technical tasks and navigating pharmacist-patient language barriers were important to intervention implementation and delivery. Identifying cost-savings and quantifying outcomes as a result of the intervention were particularly important when considering how to sustain and expand the PHARM-DC intervention. CONCLUSION: The PHARM-DC intervention can successfully be implemented at two institutions with considerable variations in TOC initiatives, resources, and staffing. Future implementation of PHARM-DC interventions should consider the themes identified, including an examination of institution-specific contextual factors such as the roles that pharmacy technicians may play in TOC interventions, the importance of intervention adaptability to account for patient needs and institutional resources, and pharmacist recommendations for intervention improvement and sustainability. TRIAL REGISTRATION: NCT04071951 .

Will Apple devices' passive atrial fibrillation detection prevent strokes? Estimating the proportion of high-risk actionable patients with real-world user data.

OBJECTIVE: Utilizing integrated electronic health record (EHR) and consumer-grade wearable device data, we sought to provide real-world estimates for the proportion of wearers that would likely benefit from anticoagulation if an atrial fibrillation (AFib) diagnosis was made based on wearable device data. MATERIALS AND METHODS: This study utilized EHR and Apple Watch data from an observational cohort of 1802 patients at Cedars-Sinai Medical Center who linked devices to the EHR between April 25, 2015 and November 16, 2018. Using these data, we estimated the number of high-risk patients who would be actionable for anticoagulation based on (1) medical history, (2) Apple Watch wear patterns, and (3) AFib risk, as determined by an existing validated model. RESULTS: Based on the characteristics of this cohort, a mean of 0.25% (n = 4.58, 95% CI, 2.0-8.0) of patients would be candidates for new anticoagulation based on AFib identified by their Apple Watch. Using EHR data alone, we find that only approximately 36% of the 1802 patients (n = 665.93, 95% CI, 626.0-706.0) would have anticoagulation recommended even after a new AFib diagnosis. DISCUSSION AND CONCLUSION: These data suggest that there is limited benefit to detect and treat AFib with anticoagulation among this cohort, but that accessing clinical and demographic data from the EHR could help target devices to the patients with the highest potential for benefit. Future research may analyze this relationship at other sites and among other wearable users, including among those who have not linked devices to their EHR.

Incidence of interruptive penicillin allergy alerts in patients with previously documented beta-lactam exposure: Potential for leveraging the electronic health record to identify erroneous allergies.

BACKGROUND: Approximately 10% of patients report allergies to penicillin, yet >90% of these allergies are not clinically significant. Patients reporting penicillin allergies are often treated with second-line, non-ß-lactam antibiotics that are typically broader spectrum and more toxic. Orders for ß-lactam antibiotics for these patients trigger interruptive alerts, even when there is electronic health record (EHR) data indicating prior ß-lactam exposure. OBJECTIVE: To describe the rate that interruptive penicillin allergy alerts display for patients who have previously had a ß-lactam exposure. DESIGN: Retrospective EHR review from January 2013 through June 2018. SETTING: A nonprofit health system including 1 large tertiary-care medical center, a smaller associated hospital, 2 emergency departments, and ˜250 outpatient clinics. PARTICIPANTS: All patients with EHR-documented of penicillin allergies. METHODS: We examined interruptive penicillin allergy alerts and identified the number and percentage of alerts that display for patients with a prior administration of a penicillin class or other ß-lactam antibiotic. RESULTS: Of 115,081 allergy alerts that displayed during the study period, 8% were displayed for patients who had an inpatient administration of a penicillin antibiotic after the allergy was noted, and 49% were displayed for patients with a prior inpatient administration of any ß-lactam. CONCLUSIONS: Many interruptive penicillin allergy alerts display for patients who would likely tolerate a penicillin, and half of all alerts display for patients who would likely tolerate another ß-lactam.

Personal Electronic Records of Medications (PERMs) for medication reconciliation at care transitions: a rapid realist review.

BACKGROUND: Medication reconciliation (MedRec), a process to reduce medication error at care transitions, is labour- and resource-intensive and time-consuming. Use of Personal Electronic Records of Medications (PERMs) in health information systems to support MedRec have proven challenging. Relatively little is known about the design, use or implementation of PERMs at care transitions that impacts on MedRec in the 'real world'. To respond to this gap in knowledge we undertook a rapid realist review (RRR). The aim was to develop theories to explain how, why, when, where and for whom PERMs are designed, implemented or used in practice at care transitions that impacts on MedRec. METHODOLOGY: We used realist methodology and undertook the RRR between August 2020 and February 2021. We collaborated with experts in the field to identify key themes. Articles were sourced from four databases (Pubmed, Embase, CINAHL Complete and OpenGrey) to contribute to the theory development. Quality assessment, screening and data extraction using NVivo was completed. Contexts, mechanisms and outcomes configurations were identified and synthesised. The experts considered these theories for relevance and practicality and suggested refinements. RESULTS: Ten provisional theories were identified from 19 articles. Some theories relate to the design (T2 Inclusive design, T3 PERMs complement existing good processes, T7 Interoperability), some relate to the implementation (T5 Tailored training, T9 Positive impact of legislation or governance), some relate to use (T6 Support and on-demand training) and others relate iteratively to all stages of the process (T1 Engage stakeholders, T4 Build trust, T8 Resource investment, T10 Patients as users of PERMs). CONCLUSIONS: This RRR has allowed additional valuable data to be extracted from existing primary research, with minimal resources, that may impact positively on future developments in this area. The theories are interdependent to a greater or lesser extent; several or all of the theories may need to be in play to collectively impact on the design, implementation or use of PERMs for MedRec at care transitions. These theories should now be incorporated into an intervention and evaluated to further test their validity.